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Record W2998225873 · doi:10.1002/anie.201913566

The Unexpected Role of Se<sup>VI</sup>Species in Epoxidations with Benzeneseleninic Acid and Hydrogen Peroxide

2019· article· en· W2998225873 on OpenAlexafffund
Kai N. Sands, Emérita Mendoza Rengifo, Graham N. George, Ingrid J. Pickering, Benjamin S. Gelfand, Thomas G. Back

Bibliographic record

VenueAngewandte Chemie International Edition · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicOrganoselenium and organotellurium chemistry
Canadian institutionsUniversity of SaskatchewanUniversity of Calgary
FundersNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaOffice of Science
KeywordsHydrogen peroxideCycloocteneChemistryPeroxideOxygenSalt (chemistry)RedoxHydrogenPhotochemistryInorganic chemistryCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Benzeneperoxyseleninic acid has been proposed as the key intermediate in the widely used epoxidation of alkenes with benzeneseleninic acid and hydrogen peroxide. However, it reacts sluggishly with cyclooctene and instead rapidly decomposes in solution to a mixed selenonium–selenonate salt that was identified by X‐ray absorption and 77 Se NMR spectroscopy, as well as by single crystal X‐ray diffraction. This process includes a selenoxide elimination of the peroxyseleninic acid with liberation of oxygen and additional redox steps. The salt is relatively stable in the solid state, but generates the corresponding selenonic acid in the presence of hydrogen peroxide. The selenonic acid is inert towards cyclooctene on its own; however, rapid epoxidation occurs when hydrogen peroxide is added. This shows that the selenonic acid must first be activated through further oxidation, presumably to the heretofore unknown benzeneperoxyselenonic acid. The latter is the principal oxidant in this epoxidation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.314
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations31
Published2019
Admission routes2
Has abstractyes

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